{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir('D:\\Practical Time Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('datasets/WDI_csv/WDIData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column names: Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n",
      "       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
      "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
      "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
      "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
      "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
      "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
      "       '2014', '2015', '2016', 'Unnamed: 61'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('Column names:', data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of rows, columns: (397056, 62)\n"
     ]
    }
   ],
   "source": [
    "print('No. of rows, columns:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique number of indicators: 1504\n"
     ]
    }
   ],
   "source": [
    "nb_indicators = data['Indicator Name'].unique().shape[0]\n",
    "print('Unique number of indicators:', nb_indicators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique number of countries: 264\n"
     ]
    }
   ],
   "source": [
    "nb_countries = data['Country Code'].unique().shape[0]\n",
    "print('Unique number of countries:', nb_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "central_govt_debt = data.loc[data['Indicator Name']=='Central government debt, total (% of GDP)']\n",
    "military_exp = data.loc[data['Indicator Name']=='Military expenditure (% of GDP)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of central_govt_debt: (264, 62)\n",
      "Shape of military_exp: (264, 62)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of central_govt_debt:', central_govt_debt.shape)\n",
    "print('Shape of military_exp:', military_exp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     93.000000\n",
       "mean      52.894412\n",
       "std       30.866372\n",
       "min        0.519274\n",
       "25%       28.030110\n",
       "50%       49.559823\n",
       "75%       75.289093\n",
       "max      168.474953\n",
       "Name: 2010, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "central_govt_debt['2010'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    194.000000\n",
       "mean       1.958123\n",
       "std        1.370594\n",
       "min        0.000000\n",
       "25%        1.120302\n",
       "50%        1.606592\n",
       "75%        2.594417\n",
       "max        8.588373\n",
       "Name: 2010, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "military_exp['2010'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "central_govt_debt.index = central_govt_debt['Country Code']\n",
    "military_exp.index = military_exp['Country Code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "central_govt_debt_2010 = central_govt_debt['2010'].loc[~pd.isnull(central_govt_debt['2010'])]\n",
    "military_exp_2010 = military_exp['2010'].loc[~pd.isnull(military_exp['2010'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_to_plot = pd.concat((central_govt_debt_2010, military_exp_2010), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_to_plot.columns = ['central_govt_debt', 'military_exp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>central_govt_debt</th>\n",
       "      <th>military_exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.897473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGO</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.244884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.558592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.122879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.119468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.814878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARM</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.265646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATG</th>\n",
       "      <td>75.289093</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>29.356946</td>\n",
       "      <td>1.951809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT</th>\n",
       "      <td>79.408304</td>\n",
       "      <td>0.824770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     central_govt_debt  military_exp\n",
       "AFG                NaN      1.897473\n",
       "AGO                NaN      4.244884\n",
       "ALB                NaN      1.558592\n",
       "ARB                NaN      5.122879\n",
       "ARE                NaN      6.119468\n",
       "ARG                NaN      0.814878\n",
       "ARM                NaN      4.265646\n",
       "ATG          75.289093           NaN\n",
       "AUS          29.356946      1.951809\n",
       "AUT          79.408304      0.824770"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_to_plot.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_to_plot = data_to_plot.loc[(~pd.isnull(data_to_plot.central_govt_debt)) & (~pd.isnull(data_to_plot.military_exp)), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>central_govt_debt</th>\n",
       "      <th>military_exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>29.356946</td>\n",
       "      <td>1.951809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT</th>\n",
       "      <td>79.408304</td>\n",
       "      <td>0.824770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZE</th>\n",
       "      <td>6.385576</td>\n",
       "      <td>2.791004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEL</th>\n",
       "      <td>7.022605</td>\n",
       "      <td>1.084631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGR</th>\n",
       "      <td>21.286254</td>\n",
       "      <td>1.765384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     central_govt_debt  military_exp\n",
       "AUS          29.356946      1.951809\n",
       "AUT          79.408304      0.824770\n",
       "AZE           6.385576      2.791004\n",
       "BEL           7.022605      1.084631\n",
       "BGR          21.286254      1.765384"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_to_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1zTZo1HJIAjB/tT2lQgpdhBy1Bit4xgL63IQMJBSsAWa1u2C2OQM6I6A9fbQa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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f3bb94860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "g = sns.distplot(np.array(data_to_plot.military_exp), norm_hist=False)\n",
    "g.set_title('Military expenditure (% of GDP) of 85 countries in 2010')\n",
    "plt.savefig('plots/ch1/B07887_01_01.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y2FgA/vjjD0aMGFEu4Cpz42dzs7/++ospU6YwatQonJ2dOXToUJXnK4C3tzdmZmYcOHAA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\n5cuXAWjWzLPKbXbu/ZnikmL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5OxtbktOSyw2vVlZxM+1qVgoqExUuNv8s23Xy2jmS8tPo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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f3b0a8c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "g = sns.kdeplot(data_to_plot.military_exp, data2=data_to_plot.central_govt_debt)\n",
    "g.set_title('Military expenditures & Debt of central governments in 2010')\n",
    "plt.savefig('plots/ch1/B07887_01_02.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "central_govt_debt_us = central_govt_debt.loc[central_govt_debt['Country Code']=='USA', :].T\n",
    "military_exp_us = military_exp.loc[military_exp['Country Code']=='USA', :].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_us = pd.concat((military_exp_us, central_govt_debt_us), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index0 = np.where(data_us.index=='1960')[0][0]\n",
    "index1 = np.where(data_us.index=='2010')[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_us = data_us.iloc[index0:index1+1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_us.columns = ['Federal Military Expenditure', 'Debt of Federal Government']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Federal Military Expenditure</th>\n",
       "      <th>Debt of Federal Government</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Federal Military Expenditure Debt of Federal Government\n",
       "1960                          NaN                        NaN\n",
       "1961                          NaN                        NaN\n",
       "1962                          NaN                        NaN\n",
       "1963                          NaN                        NaN\n",
       "1964                          NaN                        NaN\n",
       "1965                          NaN                        NaN\n",
       "1966                          NaN                        NaN\n",
       "1967                          NaN                        NaN\n",
       "1968                          NaN                        NaN\n",
       "1969                          NaN                        NaN\n",
       "1970                          NaN                        NaN\n",
       "1971                          NaN                        NaN\n",
       "1972                          NaN                        NaN\n",
       "1973                          NaN                        NaN\n",
       "1974                          NaN                        NaN\n",
       "1975                          NaN                        NaN\n",
       "1976                          NaN                        NaN\n",
       "1977                          NaN                        NaN\n",
       "1978                          NaN                        NaN\n",
       "1979                          NaN                        NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_us.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data_us: (23, 2)\n"
     ]
    }
   ],
   "source": [
    "#There are null rows for several years possibly due to unavailability of data for these years\n",
    "data_us.dropna(inplace=True)\n",
    "print('Shape of data_us:', data_us.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Federal Military Expenditure</th>\n",
       "      <th>Debt of Federal Government</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>5.57993</td>\n",
       "      <td>42.0258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>5.37472</td>\n",
       "      <td>43.1439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>5.12025</td>\n",
       "      <td>45.3772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>4.53985</td>\n",
       "      <td>48.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>4.66626</td>\n",
       "      <td>50.6016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>4.32693</td>\n",
       "      <td>50.1657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>3.94129</td>\n",
       "      <td>49.3475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>3.63849</td>\n",
       "      <td>49.2366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>3.35074</td>\n",
       "      <td>46.7174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>3.2099</td>\n",
       "      <td>43.2997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>3.01764</td>\n",
       "      <td>39.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2.90839</td>\n",
       "      <td>37.6123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2.93343</td>\n",
       "      <td>33.1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.94434</td>\n",
       "      <td>51.9937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3.24955</td>\n",
       "      <td>53.5029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>3.60729</td>\n",
       "      <td>55.9687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>3.78557</td>\n",
       "      <td>56.3841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3.84423</td>\n",
       "      <td>56.2891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3.8082</td>\n",
       "      <td>55.2942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3.84704</td>\n",
       "      <td>55.6343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Federal Military Expenditure Debt of Federal Government\n",
       "1988                      5.57993                    42.0258\n",
       "1989                      5.37472                    43.1439\n",
       "1990                      5.12025                    45.3772\n",
       "1991                      4.53985                     48.633\n",
       "1992                      4.66626                    50.6016\n",
       "1993                      4.32693                    50.1657\n",
       "1994                      3.94129                    49.3475\n",
       "1995                      3.63849                    49.2366\n",
       "1996                      3.35074                    46.7174\n",
       "1997                       3.2099                    43.2997\n",
       "1998                      3.01764                     39.977\n",
       "1999                      2.90839                    37.6123\n",
       "2000                      2.93343                    33.1582\n",
       "2001                      2.94434                    51.9937\n",
       "2002                      3.24955                    53.5029\n",
       "2003                      3.60729                    55.9687\n",
       "2004                      3.78557                    56.3841\n",
       "2005                      3.84423                    56.2891\n",
       "2006                       3.8082                    55.2942\n",
       "2007                      3.84704                    55.6343"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First 20 rows of data_us after dropping rows with missing values\n",
    "data_us.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tVktSUlKKH5vnZXU/Ozo6EhYWhtFo5PHjx+zfv988zt7e3pyEfv/99xQJ40VVrlwZjUbD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f3b4ca630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Two subplots, the axes array is 1-d\n",
    "f, axarr = plt.subplots(2, sharex=True)\n",
    "f.set_size_inches(5.5, 5.5)\n",
    "axarr[0].set_title('Federal Military Expenditure during 1988-2010 (% of GDP)')\n",
    "data_us['Federal Military Expenditure'].plot(linestyle='-', marker='*', color='b', ax=axarr[0])\n",
    "axarr[1].set_title('Debt of Federal Government during 1988-2010 (% of GDP)')\n",
    "data_us['Debt of Federal Government'].plot(linestyle='-', marker='*', color='r', ax=axarr[1])\n",
    "plt.savefig('plots/ch1/B07887_01_03.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\avpal\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "chn = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\\\n",
    "               (data['Country Code']=='CHN'),index0:index1+1\n",
    "             ]\n",
    "chn = pd.Series(data=chn.values[0], index=chn.columns)\n",
    "chn.dropna(inplace=True)\n",
    "\n",
    "usa = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\\\n",
    "               (data['Country Code']=='USA'),index0:index1+1\n",
    "             ]\n",
    "usa = pd.Series(data=usa.values[0], index=usa.columns)\n",
    "usa.dropna(inplace=True)\n",
    "\n",
    "ind = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\\\n",
    "               (data['Country Code']=='IND'),index0:index1+1\n",
    "             ]\n",
    "ind = pd.Series(data=ind.values[0], index=ind.columns)\n",
    "ind.dropna(inplace=True)\n",
    "\n",
    "gbr = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\\\n",
    "               (data['Country Code']=='GBR'),index0:index1+1\n",
    "             ]\n",
    "gbr = pd.Series(data=gbr.values[0], index=gbr.columns)\n",
    "gbr.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S6b42UlO7dm0uXLhAq1atMBqNBAQEEBQUlORzfXx8yJ49O23btrUtS+7ayJ49O926daNZ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\nkIYshBAaIQ1ZCCE0QhqyEEJoxP8DhuEyIb6wVrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f3b4407f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "usa.plot(linestyle='-', marker='*', color='b')\n",
    "chn.plot(linestyle='-', marker='*', color='r')\n",
    "gbr.plot(linestyle='-', marker='*', color='g')\n",
    "ind.plot(linestyle='-', marker='*', color='y')\n",
    "plt.legend(['USA','CHINA','UK','INDIA','RUSSIA'], loc=1)\n",
    "plt.title('Miltitary expenditure of 5 countries over 10 years')\n",
    "plt.ylabel('Military expenditure (% of GDP)')\n",
    "plt.xlabel('Years')\n",
    "plt.savefig('plots/ch1/B07887_01_04.png', format='png', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
